3.3 Single population proportion

  • The population parameter \(p\) of the Binomial distribution can also be tested using similar procedure

\[\begin{equation} H_0:~~p=p_0 \tag{3.9} \end{equation}\]

  • Assumed population proportion \(p_0\) is the value that we think is true. If sample data shows that this is false, we reject the null hypothesis. Otherwise, we do not reject the null hypothesis.

  • Three alternative hypothesis are possible:

\[\begin{align} H_1:&~~p \ne p_0 &\text{two-tailed test} \\ \\ H_1:&~~p < p_0 &\text{left-tailed test} \\ \\ H_1:&~~p > p_0 &\text{right-tailed test} \\ \tag{3.10} \end{align}\]

  • As the binomial distribution of a sample proportion \(\hat{p}\) can be approximated by the normal distribution, the test statistic follows a standard normal distribution:

\[\begin{align} Z&=\frac{\hat{p}-p_0}{\sqrt{\frac{p_0(1-p_0)}{n}}} \\ \\ \hat{p}&=\frac{x}{n} \\ \tag{3.11} \end{align}\]

Example 3.5 Suppose a consumer group suspects that the proportion of households that have three or more cell phones is \(30\%\). A cell phone company has reason to believe that the proportion is not \(30\%\). Before they start a big advertising campaign, they conduct a hypothesis test. Their marketing people survey \(150\) households with the result that \(43\) of them have three or more cell phones. Level of significance is \(5%\).

Example 3.6 Test the hypothesis that more than \(50\%\) of the companies are listed on the stock exchange. Level of significance is \(5\%\).